Triple
T17049856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main-Tauber-Kreis |
E413663
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Külsheim
Külsheim is a small town in the Main-Tauber district of Baden-Württemberg in southwestern Germany, known for its historic center and surrounding rural landscape.
|
E1292067
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Külsheim | Statement: [Main-Tauber-Kreis, contains, Külsheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Külsheim Context triple: [Main-Tauber-Kreis, contains, Külsheim]
-
A.
Höchheim
Höchheim is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany.
-
B.
Nussloch
Nussloch is a small town in southwestern Germany, known in part for hosting the headquarters of medical technology company Leica Biosystems.
-
C.
Weikersheim
Weikersheim is a small historic town in the Tauber Valley of Baden-Württemberg, Germany, known for its Renaissance castle and well-preserved old town.
-
D.
Kelkheim
Kelkheim is a town in the Main-Taunus district of Hesse, Germany, known for its furniture industry and proximity to Frankfurt.
-
E.
Griesheim
Griesheim is a town in the German state of Hesse, located near the city of Darmstadt and known for its residential character and local industry.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Külsheim Triple: [Main-Tauber-Kreis, contains, Külsheim]
Generated description
Külsheim is a small town in the Main-Tauber district of Baden-Württemberg in southwestern Germany, known for its historic center and surrounding rural landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Külsheim Target entity description: Külsheim is a small town in the Main-Tauber district of Baden-Württemberg in southwestern Germany, known for its historic center and surrounding rural landscape.
-
A.
Höchheim
Höchheim is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany.
-
B.
Nussloch
Nussloch is a small town in southwestern Germany, known in part for hosting the headquarters of medical technology company Leica Biosystems.
-
C.
Weikersheim
Weikersheim is a small historic town in the Tauber Valley of Baden-Württemberg, Germany, known for its Renaissance castle and well-preserved old town.
-
D.
Kelkheim
Kelkheim is a town in the Main-Taunus district of Hesse, Germany, known for its furniture industry and proximity to Frankfurt.
-
E.
Griesheim
Griesheim is a town in the German state of Hesse, located near the city of Darmstadt and known for its residential character and local industry.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d886cde3d481908d4d01ba88ba7eb7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3daa1aeac81909e8d97bd708c6b71 |
completed | April 18, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a030c48b4488190a197ccfd652695a9 |
completed | May 12, 2026, 11:17 a.m. |
| NEDg | Description generation | batch_6a030d22013c8190801475da925e0ab9 |
completed | May 12, 2026, 11:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a030dd233208190b7af976e68e4296c |
completed | May 12, 2026, 11:24 a.m. |
Created at: April 10, 2026, 5:34 a.m.